function [C, sigma] = dataset3Params(X, y, Xval, yval)
%DATASET3PARAMS returns your choice of C and sigma for Part 3 of the exercise
%where you select the optimal (C, sigma) learning parameters to use for SVM
%with RBF kernel
%   [C, sigma] = DATASET3PARAMS(X, y, Xval, yval) returns your choice of C and 
%   sigma. You should complete this function to return the optimal C and 
%   sigma based on a cross-validation set.
%

% You need to return the following variables correctly.
C = 1;
sigma = 0.3;

% ====================== YOUR CODE HERE ======================
% Instructions: Fill in this function to return the optimal C and sigma
%               learning parameters found using the cross validation set.
%               You can use svmPredict to predict the labels on the cross
%               validation set. For example, 
%                   predictions = svmPredict(model, Xval);
%               will return the predictions on the cross validation set.
%
%  Note: You can compute the prediction error using 
%        mean(double(predictions ~= yval))
%

% ====================== Uncomment to calculate C, sigma======
%C_list = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100, 300];
%sigma_list = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30];

%result_list = zeros(length(sigma_list) * length(C_list), 3);

%row_count = 1;
%for cv = C_list
%  for sigmav = sigma_list
%    model= svmTrain(X, y, cv, @(x1, x2) gaussianKernel(x1, x2, sigmav));
%    pred = svmPredict(model,Xval);
%    error_diff = mean(double(pred ~= yval));
%    result_list(row_count, :) = [cv, sigmav, error_diff];
%    row_count = row_count + 1;
%  endfor
%endfor
%[v, i] = min(result_list(:, 3));
%C = result_list(i, 1);
%sigma = result_list(i, 2);

C = 1;
sigma = 0.1;
% =========================================================================

end
